Plot the Models Performance on the Testing Partitions

plot_model(model.obj, model_ids = NULL)

Arguments

model.obj

A train_model object

model_ids

A character, defines the trained models to plot, if set to NULL (default), will plot all the models

Value

Animation of models forecast on the testing partitions compared to the actuals

Details

The plot_model provides a visualization of the models performance on the testing paritions for the train_model function output

Examples

# NOT RUN { # Defining the models and their arguments methods <- list(ets1 = list(method = "ets", method_arg = list(opt.crit = "lik"), notes = "ETS model with opt.crit = lik"), ets2 = list(method = "ets", method_arg = list(opt.crit = "amse"), notes = "ETS model with opt.crit = amse"), arima1 = list(method = "arima", method_arg = list(order = c(2,1,0)), notes = "ARIMA(2,1,0)"), arima2 = list(method = "arima", method_arg = list(order = c(2,1,2), seasonal = list(order = c(1,1,1))), notes = "SARIMA(2,1,2)(1,1,1)"), hw = list(method = "HoltWinters", method_arg = NULL, notes = "HoltWinters Model"), tslm = list(method = "tslm", method_arg = list(formula = input ~ trend + season), notes = "tslm model with trend and seasonal components")) # Training the models with backtesting md <- train_model(input = USgas, methods = methods, train_method = list(partitions = 6, sample.out = 12, space = 3), horizon = 12, error = "MAPE") # Plot the models performance on the testing partitions plot_model(model.obj = md) # Plot only the ETS models plot_model(model.obj = md , model_ids = c("ets1", "ets2")) # }